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Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review

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能源与人工智能(英文) 2022年第4期10卷 262-289页

作者： Paige Wenbin Tien Shuangyu Wei jo darkwa Christopher Wood john Kaiser Calautit Department of Architecture and Built Environment University of NottinghamNottingham NG72RDUK

The built environment sector is responsible for almost one-third of the world’s final energy consumption. Hence, seeking plausible solutions to minimise building energy demands and mitigate adverse environmental impacts is necessary. Artificial intelligence (AI) techniques such as machine and deep learning have been increasingly and successfully applied to develop solutions for the built environment. This review provided a critical summary of the existing literature on the machine and deep learning methods for the built environment over the past decade, with special reference to holistic approaches. Different AI-based techniques employed to resolve interconnected problems related to heating, ventilation and air conditioning (HVAC) systems and enhance building performances were reviewed, including energy forecasting and management, indoor air quality and occupancy comfort/satisfaction prediction, occupancy detection and recognition, and fault detection and diagnosis. The present study explored existing AI-based techniques focusing on the framework, methodology, and performance. The literature highlighted that selecting the most suitable machine learning and deep learning model for solving a problem could be challenging. The recent explosive growth experienced by the research area has led to hundreds of machine learning algorithms being applied to building performance-related studies. The literature showed that existing research studies considered a wide range of scope/scales (from an HVAC component to urban areas) and time scales (minute to year). This makes it difficult to find an optimal algorithm for a specific task or case. The studies also employed a wide range of evaluation metrics, adding to the challenge. Further developments and more specific guidelines are required for the built environment field to encourage best practices in evaluating and selecting models. The literature also showed that while machine and deep learning had been successfully applied in

关键词： Artificial intelligence Building energy management Deep learning Heating,ventilation and air conditioning (HVAC) Indoor environmental quality(IEQ) Machine learning Occupancy detection Thermal comfort

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Prospects of Bioenergy Production for Sustainable Rural Development in Ghana

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journal of Sustainable Bioenergy Systems 2021年第4期11卷 227-259页

作者： Nii Nelson jo darkwa john Calautit Faculty of Engineering University of Nottingham Nottingham UK

Biomass supplies about 80% of the energy needs for cooking and heating in rural Ghana. It is predominantly used in traditional and inefficient forms (firewood and charcoal), which presents environmental and health concerns. In order to better the living standard in rural Ghana, efforts must be made to provide modern energy services. Most rural communities in Ghana are so remote that an extension of the national grid is uneconomical, hence biomass electricity provides a viable alternative. Biomass is pivotal to the socio-economic development of rural Ghana due to its easy accessibility and enormous potential in the production of varied energy forms. In this paper, a comprehensive review of biomass resources, biomass energy conversion technologies and bioenergy production potential for rural development in Ghana is provided. The most important feedstock from an energy perspective was found to be crop residues. Based on 2017 statistics, Ghana has a theoretical potential of 623.84 PJ of energy from agricultural crop residues and 64.27 PJ of energy from livestock production. Evidence from literature suggests that biomass gasification is the best conversion technology to expand electricity access rate for rural households in Ghana. The paper concludes that although ample biomass resources exist, cocoa pod husks (CPH) which is very common in rural Ghana can be pelletized and used as feedstock for rural power generation systems.

关键词： Biomass Bioenergy Biofuel Feedstock Conversion Technologies

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